@inproceedings{098c5c71-ff2f-4446-a12a-1aef8d016828,
abstract = {In this paper, we study machine learning techniques<br/><br>
and features of electroencephalography activity bursts<br/><br>
for predicting outcome in extremely preterm infants. It was<br/><br>
previously shown that the distribution of interburst interval<br/><br>
durations predicts clinical outcome, but in previous work the<br/><br>
information within the bursts has been neglected. In this paper,<br/><br>
we perform exploratory analysis of feature extraction of burst<br/><br>
characteristics and use machine learning techniques to show<br/><br>
that such features could be used for outcome prediction. The<br/><br>
results are promising, but further verification of the results in<br/><br>
larger datasets is needed to obtain conclusive results.},
author = {Simayijiang, Zhayida and Backman, Sofia and Ulén, Johannes and Wikström, Sverre and Åström, Karl},
issn = {1557-170X},
language = {eng},
location = {Osaka, Japan},
pages = {4295--4298},
publisher = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
title = {Exploratory study of EEG burst characteristics in preterm infants},
url = {http://dx.doi.org/10.1109/EMBC.2013.6610495},
year = {2013},
}